Autonomous driving in a crowded environment, eg, a busy traffic intersection, is an unsolved
challenge for robotics. The robot vehicle must contend with a dynamic and partially …

Uncertainty in human behaviors poses a significant challenge to autonomous driving in
crowded urban environments. The partially observable Markov decision process (POMDP) …

Real-time planning under uncertainty is critical for robots operating in complex dynamic
environments. Consider, for example, an autonomous robot vehicle driving in dense …

Autonomous driving in urban crowds at unregulated intersections is challenging, where
dynamic occlusions and uncertain behaviors of other vehicles should be carefully …

Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially,
in the presence of many aggressive, high-speed traffic participants. This paper presents …

Machine Learning (ML) has replaced traditional handcrafted methods for perception and
prediction in autonomous vehicles. Yet for the equally important planning task, the adoption …

Learning a human-like driving policy from large-scale driving demonstrations is promising,
but the uncertainty and non-deterministic nature of planning make it challenging. In this …

Traditional autonomous vehicle pipelines that follow a modular approach have been very
successful in the past both in academia and industry, which has led to autonomy deployed …

Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging
for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of …

Seamlessly operating an autonomous vehicles in a crowded pedestrian environment is a
very challenging task. This is because human movement and interactions are very hard to …